A time series model-based method for gear tooth crack detection and severity assessment under random speed variation. (July 2021)

• Record Type: Journal Article
• Authors: Chen, Yuejian; Schmidt, Stephan; Heyns, P. Stephan; Zuo, Ming J.
• Is Part Of: Mechanical systems and signal processing. Volume 156(2021)
• Publication Date: 2021-07
• Subjects: Gearbox -- Condition monitoring -- Random speed variation -- Time series model; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.107605
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Fault detection and quantification under random speed variation are considered. A linear parameter-varying auto-regression (LPV-AR) model based method is presented. Experimental signals are used to evaluate the performance of the presented method. The presented method outperforms two existing methods. Abstract: In industry (e.g., wind power), gearboxes often operate under random speed variations. A condition monitoring system is expected to detect faults and assess their severity using vibration signals collected under different speed profiles. A few studies have been reported for condition monitoring of gearboxes under random speed variations, including a novelty diagnostic method and a support vector machine (SVM) based method. However, these methods either are based on the strict assumption that the rotating speed does not vary significantly within a rotating cycle or have the drawback of low classification accuracy. This paper presents a time series model-based method for gear tooth crack detection and severity assessment under random speed variation. Specifically, the rotating speed and phase are considered as covariates in a linear parameter varying autoregression (AR) model for representing impulsive vibration signals. We propose refined B-splines for mapping the dependency between AR coefficients and the rotating phase. The performance of the presented time series model-based method has been validated using laboratory signals. The presented method can assess 93.8% of the tooth crack severity state correctly, which is better than the novelty diagnostic method (74.4%) and SVM-based method (87.7%).